[SPARK-1402]  Added 3 more compression schemes

JIRA issue: [SPARK-1402](https://issues.apache.org/jira/browse/SPARK-1402)

This PR provides 3 more compression schemes for Spark SQL in-memory columnar storage:

* `BooleanBitSet`
* `IntDelta`
* `LongDelta`

Now there are 6 compression schemes in total, including the no-op `PassThrough` scheme.

Also fixed a bug in PR #286: not all compression schemes are added as available schemes when accessing an in-memory column, and when a column is compressed with an unrecognised scheme, `ColumnAccessor` throws exception.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #330 from liancheng/moreCompressionSchemes and squashes the following commits:

1d037b8 [Cheng Lian] Fixed SPARK-1436: in-memory column byte buffer must be able to be accessed multiple times
d7c0e8f [Cheng Lian] Added test suite for IntegralDelta (IntDelta & LongDelta)
3c1ad7a [Cheng Lian] Added test suite for BooleanBitSet, refactored other test suites
44fe4b2 [Cheng Lian] Refactored CompressionScheme, added 3 more compression schemes.
[SPARK-1103] Automatic garbage collection of RDD, shuffle and broadcast data

This PR allows Spark to automatically cleanup metadata and data related to persisted RDDs, shuffles and broadcast variables when the corresponding RDDs, shuffles and broadcast variables fall out of scope from the driver program. This is still a work in progress as broadcast cleanup has not been implemented.

**Implementation Details**
A new class `ContextCleaner` is responsible cleaning all the state. It is instantiated as part of a `SparkContext`. RDD and ShuffleDependency classes have overridden `finalize()` function that gets called whenever their instances go out of scope. The `finalize()` function enqueues the object’s identifier (i.e. RDD ID, shuffle ID, etc.) with the `ContextCleaner`, which is a very short and cheap operation and should not significantly affect the garbage collection mechanism. The `ContextCleaner`, on a different thread, performs the cleanup, whose details are given below.

*RDD cleanup:*
`ContextCleaner` calls `RDD.unpersist()` is used to cleanup persisted RDDs. Regarding metadata, the DAGScheduler automatically cleans up all metadata related to a RDD after all jobs have completed. Only the `SparkContext.persistentRDDs` keeps strong references to persisted RDDs. The `TimeStampedHashMap` used for that has been replaced by `TimeStampedWeakValueHashMap` that keeps only weak references to the RDDs, allowing them to be garbage collected.

*Shuffle cleanup:*
New BlockManager message `RemoveShuffle(<shuffle ID>)` asks the `BlockManagerMaster` and currently active `BlockManager`s to delete all the disk blocks related to the shuffle ID. `ContextCleaner` cleans up shuffle data using this message and also cleans up the metadata in the `MapOutputTracker` of the driver. The `MapOutputTracker` at the workers, that caches the shuffle metadata, maintains a `BoundedHashMap` to limit the shuffle information it caches. Refetching the shuffle information from the driver is not too costly.

*Broadcast cleanup:*
To be done. [This PR](https://github.com/apache/incubator-spark/pull/543/) adds mechanism for explicit cleanup of broadcast variables. `Broadcast.finalize()` will enqueue its own ID with ContextCleaner and the PRs mechanism will be used to unpersist the Broadcast data.

*Other cleanup:*
`ShuffleMapTask` and `ResultTask` caches tasks and used TTL based cleanup (using `TimeStampedHashMap`), so nothing got cleaned up if TTL was not set. Instead, they now use `BoundedHashMap` to keep a limited number of map output information. Cost of repopulating the cache if necessary is very small.

**Current state of implementation**
Implemented RDD and shuffle cleanup. Things left to be done are.
- Cleaning up for broadcast variable still to be done.
- Automatic cleaning up keys with empty weak refs as values in `TimeStampedWeakValueHashMap`

Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Andrew Or <andrewor14@gmail.com>
Author: Roman Pastukhov <ignatich@mail.ru>

Closes #126 from tdas/state-cleanup and squashes the following commits:

61b8d6e [Tathagata Das] Fixed issue with Tachyon + new BlockManager methods.
f489fdc [Tathagata Das] Merge remote-tracking branch 'apache/master' into state-cleanup
d25a86e [Tathagata Das] Fixed stupid typo.
cff023c [Tathagata Das] Fixed issues based on Andrew's comments.
4d05314 [Tathagata Das] Scala style fix.
2b95b5e [Tathagata Das] Added more documentation on Broadcast implementations, specially which blocks are told about to the driver. Also, fixed Broadcast API to hide destroy functionality.
41c9ece [Tathagata Das] Added more unit tests for BlockManager, DiskBlockManager, and ContextCleaner.
6222697 [Tathagata Das] Fixed bug and adding unit test for removeBroadcast in BlockManagerSuite.
104a89a [Tathagata Das] Fixed failing BroadcastSuite unit tests by introducing blocking for removeShuffle and removeBroadcast in BlockManager*
a430f06 [Tathagata Das] Fixed compilation errors.
b27f8e8 [Tathagata Das] Merge pull request #3 from andrewor14/cleanup
cd72d19 [Andrew Or] Make automatic cleanup configurable (not documented)
ada45f0 [Andrew Or] Merge branch 'state-cleanup' of github.com:tdas/spark into cleanup
a2cc8bc [Tathagata Das] Merge remote-tracking branch 'apache/master' into state-cleanup
c5b1d98 [Andrew Or] Address Patrick's comments
a6460d4 [Andrew Or] Merge github.com:apache/spark into cleanup
762a4d8 [Tathagata Das] Merge pull request #1 from andrewor14/cleanup
f0aabb1 [Andrew Or] Correct semantics for TimeStampedWeakValueHashMap + add tests
5016375 [Andrew Or] Address TD's comments
7ed72fb [Andrew Or] Fix style test fail + remove verbose test message regarding broadcast
634a097 [Andrew Or] Merge branch 'state-cleanup' of github.com:tdas/spark into cleanup
7edbc98 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into state-cleanup
8557c12 [Andrew Or] Merge github.com:apache/spark into cleanup
e442246 [Andrew Or] Merge github.com:apache/spark into cleanup
88904a3 [Andrew Or] Make TimeStampedWeakValueHashMap a wrapper of TimeStampedHashMap
fbfeec8 [Andrew Or] Add functionality to query executors for their local BlockStatuses
34f436f [Andrew Or] Generalize BroadcastBlockId to remove BroadcastHelperBlockId
0d17060 [Andrew Or] Import, comments, and style fixes (minor)
c92e4d9 [Andrew Or] Merge github.com:apache/spark into cleanup
f201a8d [Andrew Or] Test broadcast cleanup in ContextCleanerSuite + remove BoundedHashMap
e95479c [Andrew Or] Add tests for unpersisting broadcast
544ac86 [Andrew Or] Clean up broadcast blocks through BlockManager*
d0edef3 [Andrew Or] Add framework for broadcast cleanup
ba52e00 [Andrew Or] Refactor broadcast classes
c7ccef1 [Andrew Or] Merge branch 'bc-unpersist-merge' of github.com:ignatich/incubator-spark into cleanup
6c9dcf6 [Tathagata Das] Added missing Apache license
d2f8b97 [Tathagata Das] Removed duplicate unpersistRDD.
a007307 [Tathagata Das] Merge remote-tracking branch 'apache/master' into state-cleanup
620eca3 [Tathagata Das] Changes based on PR comments.
f2881fd [Tathagata Das] Changed ContextCleaner to use ReferenceQueue instead of finalizer
e1fba5f [Tathagata Das] Style fix
892b952 [Tathagata Das] Removed use of BoundedHashMap, and made BlockManagerSlaveActor cleanup shuffle metadata in MapOutputTrackerWorker.
a7260d3 [Tathagata Das] Added try-catch in context cleaner and null value cleaning in TimeStampedWeakValueHashMap.
e61daa0 [Tathagata Das] Modifications based on the comments on PR 126.
ae9da88 [Tathagata Das] Removed unncessary TimeStampedHashMap from DAGScheduler, added try-catches in finalize() methods, and replaced ArrayBlockingQueue to LinkedBlockingQueue to avoid blocking in Java's finalizing thread.
cb0a5a6 [Tathagata Das] Fixed docs and styles.
a24fefc [Tathagata Das] Merge remote-tracking branch 'apache/master' into state-cleanup
8512612 [Tathagata Das] Changed TimeStampedHashMap to use WrappedJavaHashMap.
e427a9e [Tathagata Das] Added ContextCleaner to automatically clean RDDs and shuffles when they fall out of scope. Also replaced TimeStampedHashMap to BoundedHashMaps and TimeStampedWeakValueHashMap for the necessary hashmap behavior.
80dd977 [Roman Pastukhov] Fix for Broadcast unpersist patch.
1e752f1 [Roman Pastukhov] Added unpersist method to Broadcast.
[SPARK-1331] Added graceful shutdown to Spark Streaming

Current version of StreamingContext.stop() directly kills all the data receivers (NetworkReceiver) without waiting for the data already received to be persisted and processed. This PR provides the fix. Now, when the StreamingContext.stop() is called, the following sequence of steps will happen.
1. The driver will send a stop signal to all the active receivers.
2. Each receiver, when it gets a stop signal from the driver, first stop receiving more data, then waits for the thread that persists data blocks to BlockManager to finish persisting all receive data, and finally quits.
3. After all the receivers have stopped, the driver will wait for the Job Generator and Job Scheduler to finish processing all the received data.

It also fixes the semantics of StreamingContext.start and stop. It will throw appropriate errors and warnings if stop() is called before start(), stop() is called twice, etc.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #247 from tdas/graceful-shutdown and squashes the following commits:

61c0016 [Tathagata Das] Updated MIMA binary check excludes.
ae1d39b [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into graceful-shutdown
6b59cfc [Tathagata Das] Minor changes based on Andrew's comment on PR.
d0b8d65 [Tathagata Das] Reduced time taken by graceful shutdown unit test.
f55bc67 [Tathagata Das] Fix scalastyle
c69b3a7 [Tathagata Das] Updates based on Patrick's comments.
c43b8ae [Tathagata Das] Added graceful shutdown to Spark Streaming.
[SPARK-1396] Properly cleanup DAGScheduler on job cancellation.

Previously, when jobs were cancelled, not all of the state in the
DAGScheduler was cleaned up, leading to a slow memory leak in the
DAGScheduler.  As we expose easier ways to cancel jobs, it's more
important to fix these issues.

This commit also fixes a second and less serious problem, which is that
previously, when a stage failed, not all of the appropriate stages
were cancelled.  See the "failure of stage used by two jobs" test
for an example of this.  This just meant that extra work was done, and is
not a correctness problem.

This commit adds 3 tests.  “run shuffle with map stage failure” is
a new test to more thoroughly test this functionality, and passes on
both the old and new versions of the code.  “trivial job
cancellation” fails on the old code because all state wasn’t cleaned
up correctly when jobs were cancelled (we didn’t remove the job from
resultStageToJob).  “failure of stage used by two jobs” fails on the
old code because taskScheduler.cancelTasks wasn’t called for one of
the stages (see test comments).

This should be checked in before #246, which makes it easier to
cancel stages / jobs.

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #305 from kayousterhout/incremental_abort_fix and squashes the following commits:

f33d844 [Kay Ousterhout] Mark review comments
9217080 [Kay Ousterhout] Properly cleanup DAGScheduler on job cancellation.
Remove extra semicolon in import statement and unused import in ApplicationMaster

Small nit cleanup to remove extra semicolon and unused import in Yarn's stable ApplicationMaster (it bothers me every time I saw it)

Author: Henry Saputra <hsaputra@apache.org>

Closes #358 from hsaputra/nitcleanup_removesemicolon_import_applicationmaster and squashes the following commits:

bffb685 [Henry Saputra] Remove extra semicolon in import statement and unused import in ApplicationMaster.scala
SPARK-1348 binding Master, Worker, and App Web UI to all interfaces

Author: Kan Zhang <kzhang@apache.org>

Closes #318 from kanzhang/SPARK-1348 and squashes the following commits:

e625a5f [Kan Zhang] reverting the changes to startJettyServer()
7a8084e [Kan Zhang] SPARK-1348 binding Master, Worker, and App Web UI to all interfaces
SPARK-1445: compute-classpath should not print error if lib_managed not found

This was added to the check for the assembly jar, forgot it for the datanucleus jars.

Author: Aaron Davidson <aaron@databricks.com>

Closes #361 from aarondav/cc and squashes the following commits:

8facc16 [Aaron Davidson] SPARK-1445: compute-classpath should not print error if lib_managed not found
[SPARK-1397] Notify SparkListeners when stages fail or are cancelled.

[I wanted to post this for folks to comment but it depends on (and thus includes the changes in) a currently outstanding PR, #305.  You can look at just the second commit: https://github.com/kayousterhout/spark-1/commit/93f08baf731b9eaf5c9792a5373560526e2bccac to see just the changes relevant to this PR]

Previously, when stages fail or get cancelled, the SparkListener is only notified
indirectly through the SparkListenerJobEnd, where we sometimes pass in a single
stage that failed.  This worked before job cancellation, because jobs would only fail
due to a single stage failure.  However, with job cancellation, multiple running stages
can fail when a job gets cancelled.  Right now, this is not handled correctly, which
results in stages that get stuck in the “Running Stages” window in the UI even
though they’re dead.

This PR changes the SparkListenerStageCompleted event to a SparkListenerStageEnded
event, and uses this event to tell SparkListeners when stages fail in addition to when
they complete successfully.  This change is NOT publicly backward compatible for two
reasons.  First, it changes the SparkListener interface.  We could alternately add a new event,
SparkListenerStageFailed, and keep the existing SparkListenerStageCompleted.  However,
this is less consistent with the listener events for tasks / jobs ending, and will result in some
code duplication for listeners (because failed and completed stages are handled in similar
ways).  Note that I haven’t finished updating the JSON code to correctly handle the new event
because I’m waiting for feedback on whether this is a good or bad idea (hence the “WIP”).

It is also not backwards compatible because it changes the publicly visible JobWaiter.jobFailed()
method to no longer include a stage that caused the failure.  I think this change should definitely
stay, because with cancellation (as described above), a failure isn’t necessarily caused by a
single stage.

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #309 from kayousterhout/stage_cancellation and squashes the following commits:

5533ecd [Kay Ousterhout] Fixes in response to Mark's review
320c7c7 [Kay Ousterhout] Notify SparkListeners when stages fail or are cancelled.
SPARK-1433: Upgrade Mesos dependency to 0.17.0

Mesos 0.13.0 was released 6 months ago.
Upgrade Mesos dependency to 0.17.0

Author: Sandeep <sandeep@techaddict.me>

Closes #355 from techaddict/mesos_update and squashes the following commits:

f1abeee [Sandeep] SPARK-1433: Upgrade Mesos dependency to 0.17.0 Mesos 0.13.0 was released 6 months ago. Upgrade Mesos dependency to 0.17.0
Spark 1271: Co-Group and Group-By should pass Iterable[X]

Author: Holden Karau <holden@pigscanfly.ca>

Closes #242 from holdenk/spark-1320-cogroupandgroupshouldpassiterator and squashes the following commits:

f289536 [Holden Karau] Fix bad merge, should have been Iterable rather than Iterator
77048f8 [Holden Karau] Fix merge up to master
d3fe909 [Holden Karau] use toSeq instead
7a092a3 [Holden Karau] switch resultitr to resultiterable
eb06216 [Holden Karau] maybe I should have had a coffee first. use correct import for guava iterables
c5075aa [Holden Karau] If guava 14 had iterables
2d06e10 [Holden Karau] Fix Java 8 cogroup tests for the new API
11e730c [Holden Karau] Fix streaming tests
66b583d [Holden Karau] Fix the core test suite to compile
4ed579b [Holden Karau] Refactor from iterator to iterable
d052c07 [Holden Karau] Python tests now pass with iterator pandas
3bcd81d [Holden Karau] Revert "Try and make pickling list iterators work"
cd1e81c [Holden Karau] Try and make pickling list iterators work
c60233a [Holden Karau] Start investigating moving to iterators for python API like the Java/Scala one. tl;dr: We will have to write our own iterator since the default one doesn't pickle well
88a5cef [Holden Karau] Fix cogroup test in JavaAPISuite for streaming
a5ee714 [Holden Karau] oops, was checking wrong iterator
e687f21 [Holden Karau] Fix groupbykey test in JavaAPISuite of streaming
ec8cc3e [Holden Karau] Fix test issues\!
4b0eeb9 [Holden Karau] Switch cast in PairDStreamFunctions
fa395c9 [Holden Karau] Revert "Add a join based on the problem in SVD"
ec99e32 [Holden Karau] Revert "Revert this but for now put things in list pandas"
b692868 [Holden Karau] Revert
7e533f7 [Holden Karau] Fix the bug
8a5153a [Holden Karau] Revert me, but we have some stuff to debug
b4e86a9 [Holden Karau] Add a join based on the problem in SVD
c4510e2 [Holden Karau] Revert this but for now put things in list pandas
b4e0b1d [Holden Karau] Fix style issues
71e8b9f [Holden Karau] I really need to stop calling size on iterators, it is the path of sadness.
b1ae51a [Holden Karau] Fix some of the types in the streaming JavaAPI suite. Probably still needs more work
37888ec [Holden Karau] core/tests now pass
249abde [Holden Karau] org.apache.spark.rdd.PairRDDFunctionsSuite passes
6698186 [Holden Karau] Revert "I think this might be a bad rabbit hole. Started work to make CoGroupedRDD use iterator and then went crazy"
fe992fe [Holden Karau] hmmm try and fix up basic operation suite
172705c [Holden Karau] Fix Java API suite
caafa63 [Holden Karau] I think this might be a bad rabbit hole. Started work to make CoGroupedRDD use iterator and then went crazy
88b3329 [Holden Karau] Fix groupbykey to actually give back an iterator
4991af6 [Holden Karau] Fix some tests
be50246 [Holden Karau] Calling size on an iterator is not so good if we want to use it after
687ffbc [Holden Karau] This is the it compiles point of replacing Seq with Iterator and JList with JIterator in the groupby and cogroup signatures
[SPARK-1434] [MLLIB] change labelParser from anonymous function to trait

This is a patch to address @mateiz 's comment in https://github.com/apache/spark/pull/245

MLUtils#loadLibSVMData uses an anonymous function for the label parser. Java users won't like it. So I make a trait for LabelParser and provide two implementations: binary and multiclass.

Author: Xiangrui Meng <meng@databricks.com>

Closes #345 from mengxr/label-parser and squashes the following commits:

ac44409 [Xiangrui Meng] use singleton objects for label parsers
3b1a7c6 [Xiangrui Meng] add tests for label parsers
c2e571c [Xiangrui Meng] rename LabelParser.apply to LabelParser.parse use extends for singleton
11c94e0 [Xiangrui Meng] add return types
7f8eb36 [Xiangrui Meng] change labelParser from annoymous function to trait
Spark-939: allow user jars to take precedence over spark jars

I still need to do a small bit of re-factoring [mostly the one Java file I'll switch it back to a Scala file and use it in both the close loaders], but comments on other things I should do would be great.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #217 from holdenk/spark-939-allow-user-jars-to-take-precedence-over-spark-jars and squashes the following commits:

cf0cac9 [Holden Karau] Fix the executorclassloader
1955232 [Holden Karau] Fix long line in TestUtils
8f89965 [Holden Karau] Fix tests for new class name
7546549 [Holden Karau] CR feedback, merge some of the testutils methods down, rename the classloader
644719f [Holden Karau] User the class generator for the repl class loader tests too
f0b7114 [Holden Karau] Fix the core/src/test/scala/org/apache/spark/executor/ExecutorURLClassLoaderSuite.scala tests
204b199 [Holden Karau] Fix the generated classes
9f68f10 [Holden Karau] Start rewriting the ExecutorURLClassLoaderSuite to not use the hard coded classes
858aba2 [Holden Karau] Remove a bunch of test junk
261aaee [Holden Karau] simplify executorurlclassloader a bit
7a7bf5f [Holden Karau] CR feedback
d4ae848 [Holden Karau] rewrite component into scala
aa95083 [Holden Karau] CR feedback
7752594 [Holden Karau] re-add https comment
a0ef85a [Holden Karau] Fix style issues
125ea7f [Holden Karau] Easier to just remove those files, we don't need them
bb8d179 [Holden Karau] Fix issues with the repl class loader
241b03d [Holden Karau] fix my rat excludes
a343350 [Holden Karau] Update rat-excludes and remove a useless file
d90d217 [Holden Karau] Fix fall back with custom class loader and add a test for it
4919bf9 [Holden Karau] Fix parent calling class loader issue
8a67302 [Holden Karau] Test are good
9e2d236 [Holden Karau] It works comrade
691ee00 [Holden Karau] It works ish
dc4fe44 [Holden Karau] Does not depend on being in my home directory
47046ff [Holden Karau] Remove bad import'
22d83cb [Holden Karau] Add a test suite for the executor url class loader suite
7ef4628 [Holden Karau] Clean up
792d961 [Holden Karau] Almost works
16aecd1 [Holden Karau] Doesn't quite work
8d2241e [Holden Karau] Adda FakeClass for testing ClassLoader precedence options
648b559 [Holden Karau] Both class loaders compile. Now for testing
e1d9f71 [Holden Karau] One loader workers.
[SPARK-1390] Refactoring of matrices backed by RDDs

This is to refactor interfaces for matrices backed by RDDs. It would be better if we have a clear separation of local matrices and those backed by RDDs. Right now, we have

1. `org.apache.spark.mllib.linalg.SparseMatrix`, which is a wrapper over an RDD of matrix entries, i.e., coordinate list format.
2. `org.apache.spark.mllib.linalg.TallSkinnyDenseMatrix`, which is a wrapper over RDD[Array[Double]], i.e. row-oriented format.

We will see naming collision when we introduce local `SparseMatrix`, and the name `TallSkinnyDenseMatrix` is not exact if we switch to `RDD[Vector]` from `RDD[Array[Double]]`. It would be better to have "RDD" in the class name to suggest that operations may trigger jobs.

The proposed names are (all under `org.apache.spark.mllib.linalg.rdd`):

1. `RDDMatrix`: trait for matrices backed by one or more RDDs
2. `CoordinateRDDMatrix`: wrapper of `RDD[(Long, Long, Double)]`
3. `RowRDDMatrix`: wrapper of `RDD[Vector]` whose rows do not have special ordering
4. `IndexedRowRDDMatrix`: wrapper of `RDD[(Long, Vector)]` whose rows are associated with indices

The current code also introduces local matrices.

Author: Xiangrui Meng <meng@databricks.com>

Closes #296 from mengxr/mat and squashes the following commits:

24d8294 [Xiangrui Meng] fix for groupBy returning Iterable
bfc2b26 [Xiangrui Meng] merge master
8e4f1f5 [Xiangrui Meng] Merge branch 'master' into mat
0135193 [Xiangrui Meng] address Reza's comments
03cd7e1 [Xiangrui Meng] add pca/gram to IndexedRowMatrix add toBreeze to DistributedMatrix for test simplify tests
b177ff1 [Xiangrui Meng] address Matei's comments
be119fe [Xiangrui Meng] rename m/n to numRows/numCols for local matrix add tests for matrices
b881506 [Xiangrui Meng] rename SparkPCA/SVD to TallSkinnyPCA/SVD
e7d0d4a [Xiangrui Meng] move IndexedRDDMatrixRow to IndexedRowRDDMatrix
0d1491c [Xiangrui Meng] fix test errors
a85262a [Xiangrui Meng] rename RDDMatrixRow to IndexedRDDMatrixRow
b8b6ac3 [Xiangrui Meng] Remove old code
4cf679c [Xiangrui Meng] port pca to RowRDDMatrix, and add multiply and covariance
7836e2f [Xiangrui Meng] initial refactoring of matrices backed by RDDs
SPARK-1093: Annotate developer and experimental API's

This patch marks some existing classes as private[spark] and adds two types of API annotations:
- `EXPERIMENTAL API` = experimental user-facing module
- `DEVELOPER API - UNSTABLE` = developer-facing API that might change

There is some discussion of the different mechanisms for doing this here:
https://issues.apache.org/jira/browse/SPARK-1081

I was pretty aggressive with marking things private. Keep in mind that if we want to open something up in the future we can, but we can never reduce visibility.

A few notes here:
- In the past we've been inconsistent with the visiblity of the X-RDD classes. This patch marks them private whenever there is an existing function in RDD that can directly creat them (e.g. CoalescedRDD and rdd.coalesce()). One trade-off here is users can't subclass them.
- Noted that compression and serialization formats don't have to be wire compatible across versions.
- Compression codecs and serialization formats are semi-private as users typically don't instantiate them directly.
- Metrics sources are made private - user only interacts with them through Spark's reflection

Author: Patrick Wendell <pwendell@gmail.com>
Author: Andrew Or <andrewor14@gmail.com>

Closes #274 from pwendell/private-apis and squashes the following commits:

44179e4 [Patrick Wendell] Merge remote-tracking branch 'apache-github/master' into private-apis
042c803 [Patrick Wendell] spark.annotations -> spark.annotation
bfe7b52 [Patrick Wendell] Adding experimental for approximate counts
8d0c873 [Patrick Wendell] Warning in SparkEnv
99b223a [Patrick Wendell] Cleaning up annotations
e849f64 [Patrick Wendell] Merge pull request #2 from andrewor14/annotations
982a473 [Andrew Or] Generalize jQuery matching for non Spark-core API docs
a01c076 [Patrick Wendell] Merge pull request #1 from andrewor14/annotations
c1bcb41 [Andrew Or] DeveloperAPI -> DeveloperApi
0d48908 [Andrew Or] Comments and new lines (minor)
f3954e0 [Andrew Or] Add identifier tags in comments to work around scaladocs bug
99192ef [Andrew Or] Dynamically add badges based on annotations
824011b [Andrew Or] Add support for injecting arbitrary JavaScript to API docs
037755c [Patrick Wendell] Some changes after working with andrew or
f7d124f [Patrick Wendell] Small fixes
c318b24 [Patrick Wendell] Use CSS styles
e4c76b9 [Patrick Wendell] Logging
f390b13 [Patrick Wendell] Better visibility for workaround constructors
d6b0afd [Patrick Wendell] Small chang to existing constructor
403ba52 [Patrick Wendell] Style fix
870a7ba [Patrick Wendell] Work around for SI-8479
7fb13b2 [Patrick Wendell] Changes to UnionRDD and EmptyRDD
4a9e90c [Patrick Wendell] EXPERIMENTAL API --> EXPERIMENTAL
c581dce [Patrick Wendell] Changes after building against Shark.
8452309 [Patrick Wendell] Style fixes
1ed27d2 [Patrick Wendell] Formatting and coloring of badges
cd7a465 [Patrick Wendell] Code review feedback
2f706f1 [Patrick Wendell] Don't use floats
542a736 [Patrick Wendell] Small fixes
cf23ec6 [Patrick Wendell] Marking GraphX as alpha
d86818e [Patrick Wendell] Another naming change
5a76ed6 [Patrick Wendell] More visiblity clean-up
42c1f09 [Patrick Wendell] Using better labels
9d48cbf [Patrick Wendell] Initial pass
[SPARK-1357] [MLLIB] Annotate developer and experimental APIs

Annotate developer and experimental APIs in MLlib.

Author: Xiangrui Meng <meng@databricks.com>

Closes #298 from mengxr/api and squashes the following commits:

13390e8 [Xiangrui Meng] Merge branch 'master' into api
dc4cbb3 [Xiangrui Meng] mark distribute matrices experimental
6b9f8e2 [Xiangrui Meng] add Experimental annotation
8773d0d [Xiangrui Meng] add DeveloperApi annotation
da31733 [Xiangrui Meng] update developer and experimental tags
555e0fe [Xiangrui Meng] Merge branch 'master' into api
ef1a717 [Xiangrui Meng] mark some constructors private add default parameters to JavaDoc
00ffbcc [Xiangrui Meng] update tree API annotation
0b674fa [Xiangrui Meng] mark decision tree APIs
86b9e34 [Xiangrui Meng] one pass over APIs of GLMs, NaiveBayes, and ALS
f21d862 [Xiangrui Meng] Merge branch 'master' into api
2b133d6 [Xiangrui Meng] intial annotation of developer and experimental apis
SPARK-1407 drain event queue before stopping event logger

Author: Kan Zhang <kzhang@apache.org>

Closes #366 from kanzhang/SPARK-1407 and squashes the following commits:

cd0629f [Kan Zhang] code refactoring and adding test
b073ee6 [Kan Zhang] SPARK-1407 drain event queue before stopping event logger
[SPARK-1357 (fix)] remove empty line after :: DeveloperApi/Experimental ::

Remove empty line after :: DeveloperApi/Experimental :: in comments to make the original doc show up in the preview of the generated html docs. Thanks @andrewor14 !

Author: Xiangrui Meng <meng@databricks.com>

Closes #373 from mengxr/api and squashes the following commits:

9c35bdc [Xiangrui Meng] remove the empty line after :: DeveloperApi/Experimental ::
SPARK-729:  Closures not always serialized at capture time

[SPARK-729](https://spark-project.atlassian.net/browse/SPARK-729) concerns when free variables in closure arguments to transformations are captured.  Currently, it is possible for closures to get the environment in which they are serialized (not the environment in which they are created).  There are a few possible approaches to solving this problem and this PR will discuss some of them.  The approach I took has the advantage of being simple, obviously correct, and minimally-invasive, but it preserves something that has been bothering me about Spark's closure handling, so I'd like to discuss an alternative and get some feedback on whether or not it is worth pursuing.

## What I did

The basic approach I took depends on the work I did for #143, and so this PR is based atop that.  Specifically: #143 modifies `ClosureCleaner.clean` to preemptively determine whether or not closures are serializable immediately upon closure cleaning (rather than waiting for an job involving that closure to be scheduled).  Thus non-serializable closure exceptions will be triggered by the line defining the closure rather than triggered where the closure is used.

Since the easiest way to determine whether or not a closure is serializable is to attempt to serialize it, the code in #143 is creating a serialized closure as part of `ClosureCleaner.clean`.  `clean` currently modifies its argument, but the method in `SparkContext` that wraps it to return a value (a reference to the modified-in-place argument).  This branch modifies `ClosureCleaner.clean` so that it returns a value:  if it is cleaning a serializable closure, it returns the result of deserializing its serialized argument; therefore it is returning a closure with an environment captured at cleaning time.  `SparkContext.clean` then returns the result of `ClosureCleaner.clean`, rather than a reference to its modified-in-place argument.

I've added tests for this behavior (777a1bc).  The pull request as it stands, given the changes in #143, is nearly trivial.  There is some overhead from deserializing the closure, but it is minimal and the benefit of obvious operational correctness (vs. a more sophisticated but harder-to-validate transformation in `ClosureCleaner`) seems pretty important.  I think this is a fine way to solve this problem, but it's not perfect.

## What we might want to do

The thing that has been bothering me about Spark's handling of closures is that it seems like we should be able to statically ensure that cleaning and serialization happen exactly once for a given closure.  If we serialize a closure in order to determine whether or not it is serializable, we should be able to hang on to the generated byte buffer and use it instead of re-serializing the closure later.  By replacing closures with instances of a sum type that encodes whether or not a closure has been cleaned or serialized, we could handle clean, to-be-cleaned, and serialized closures separately with case matches.  Here's a somewhat-concrete sketch (taken from my git stash) of what this might look like:

```scala
package org.apache.spark.util

import java.nio.ByteBuffer
import scala.reflect.ClassManifest

sealed abstract class ClosureBox[T] { def func: T }
final case class RawClosure[T](func: T) extends ClosureBox[T] {}
final case class CleanedClosure[T](func: T) extends ClosureBox[T] {}
final case class SerializedClosure[T](func: T, bytebuf: ByteBuffer) extends ClosureBox[T] {}

object ClosureBoxImplicits {
  implicit def closureBoxFromFunc[T <: AnyRef](fun: T) = new RawClosure[T](fun)
}
```

With these types declared, we'd be able to change `ClosureCleaner.clean` to take a `ClosureBox[T=>U]` (possibly generated by implicit conversion) and return a `ClosureBox[T=>U]` (either a `CleanedClosure[T=>U]` or a `SerializedClosure[T=>U]`, depending on whether or not serializability-checking was enabled) instead of a `T=>U`.  A case match could thus short-circuit cleaning or serializing closures that had already been cleaned or serialized (both in `ClosureCleaner` and in the closure serializer).  Cleaned-and-serialized closures would be represented by a boxed tuple of the original closure and a serialized copy (complete with an environment quiesced at transformation time).  Additional implicit conversions could convert from `ClosureBox` instances to the underlying function type where appropriate.  Tracking this sort of state in the type system seems like the right thing to do to me.

### Why we might not want to do that

_It's pretty invasive._  Every function type used by every `RDD` subclass would have to change to reflect that they expected a `ClosureBox[T=>U]` instead of a `T=>U`.  This obscures what's going on and is not a little ugly.  Although I really like the idea of using the type system to enforce the clean-or-serialize once discipline, it might not be worth adding another layer of types (even if we could hide some of the extra boilerplate with judicious application of implicit conversions).

_It statically guarantees a property whose absence is unlikely to cause any serious problems as it stands._  It appears that all closures are currently dynamically cleaned once and it's not obvious that repeated closure-cleaning is likely to be a problem in the future.  Furthermore, serializing closures is relatively cheap, so doing it once to check for serialization and once again to actually ship them across the wire doesn't seem like a big deal.

Taken together, these seem like a high price to pay for statically guaranteeing that closures are operated upon only once.

## Other possibilities

I felt like the serialize-and-deserialize approach was best due to its obvious simplicity.  But it would be possible to do a more sophisticated transformation within `ClosureCleaner.clean`.  It might also be possible for `clean` to modify its argument in a way so that whether or not a given closure had been cleaned would be apparent upon inspection; this would buy us some of the operational benefits of the `ClosureBox` approach but not the static cleanliness.

I'm interested in any feedback or discussion on whether or not the problems with the type-based approach indeed outweigh the advantage, as well as of approaches to this issue and to closure handling in general.

Author: William Benton <willb@redhat.com>

Closes #189 from willb/spark-729 and squashes the following commits:

f4cafa0 [William Benton] Stylistic changes and cleanups
b3d9c86 [William Benton] Fixed style issues in tests
9b56ce0 [William Benton] Added array-element capture test
97e9d91 [William Benton] Split closure-serializability failure tests
12ef6e3 [William Benton] Skip proactive closure capture for runJob
8ee3ee7 [William Benton] Predictable closure environment capture
12c63a7 [William Benton] Added tests for variable capture in closures
d6e8dd6 [William Benton] Don't check serializability of DStream transforms.
4ecf841 [William Benton] Make proactive serializability checking optional.
d8df3db [William Benton] Adds proactive closure-serializablilty checking
21b4b06 [William Benton] Test cases for SPARK-897.
d5947b3 [William Benton] Ensure assertions in Graph.apply are asserted.
SPARK-1446: Spark examples should not do a System.exit

Spark examples should exit nice using SparkContext.stop() method, rather than System.exit
System.exit can cause issues like in SPARK-1407

Author: Sandeep <sandeep@techaddict.me>

Closes #370 from techaddict/1446 and squashes the following commits:

e9234cf [Sandeep] SPARK-1446: Spark examples should not do a System.exit Spark examples should exit nice using SparkContext.stop() method, rather than System.exit System.exit can cause issues like in SPARK-1407
Revert "SPARK-729:  Closures not always serialized at capture time"

This reverts commit 8ca3b2bc90a63b23a03f339e390174cd7a672b40.